The present invention relates to a system and a method for control and/or analytics of an industrial process and more particularly to a system and a method for prioritization of transmission of process data from plant-side automation and/or processing units to processing units external to the plant.
The following discussion of related art is provided to assist the reader in understanding the advantages of the invention, and is not to be construed as an admission that this related art is prior art to this invention.
A plurality of plants that undertake process control generally fulfill simple automation and closed-loop control technology tasks. Commonly these tasks are carried out by automation units that are installed on site and thus in the vicinity of the process to be automated. Often such plants have a plurality of smaller automation units, oftentimes also spatially separated from one another, which then results in the individual process tasks also running in a distributed manner. Because of their restricted computing power, such smaller automation units tend not to be capable of mapping complex closed-loop control structures or closed-loop control and/or simulation strategies, as are possible in the higher classes of automation device. Such more complex closed-loop control strategies, which can require a significant computing capacity, can for example be so-called Model Predictive Controls (MPC), as are preferably employed in process engineering processes. Frequently the desire is also to set up complex closed-loop controls that are based on comprehensive historical data, and to use these for example in so-called Support Vector Machines (SVM), in order to be able to make optimizations to the process on this basis. Therefore such processing-intensive process engineering processes or data analysis models are frequently automated in the superordinate control and monitoring system of the plant.
We are currently experiencing a trend in the direction of central data analytics in external processing units (so-called cloud based analytics). With these external processing units cloud-based process controls for an industrial plant, the process data is collected from a plant in order to then provide it to an external processing unit for analysis. The analysis result is returned to the plant for improving the process control and process optimization. Because of its comprehensive analytics methods and the mostly self-learning techniques, cloud-based analytics allows a significant enhancement of the process controls. However the cloud-processing approaches are often not real-time-capable, because large amounts of data must be transferred from sensors or actuators of the industrial process or internally-formed data from the plant into the external processing unit, in order to analyze it there. Thereafter the analytics result is to be returned for further actions in order for it to become effective for the processes in the plant, which overall means unacceptable time delays. Closed-loop control circuits—especially when, because of the closed-loop control speed, comparatively fast sampling rates become necessary—are problematic with cloud-based methods on account of the uncertain but at least slightly deterministic communication delays. The major sources of such time delays lie a) in the data acquisition, the pre-processing and the compression, b) in the transmission of data into the cloud and c) in the analytics and result computation itself. To ameliorate the problem of latency times in cloud-based systems, attempts are being made to make data collection possible by suitable faster hardware. Furthermore it is proposed that the data undergoes processing in order for only a reduced amount of data then to be transmitted into the cloud-based system. A further point in the approach relates to the transmission of the data, in that attempts are being made to provide faster transmission channels with corresponding bandwidths. As part of the analytics itself the cloud-based systems are equipped with high-end computers running efficient algorithms.
It has been shown that high bandwidths and low wait times alone are often not sufficient. This is especially true when critical industrial processes and large amounts of data to be transmitted are involved.
It would therefore be desirable and advantageous to specify an alternate facility and an alternate method to obviate prior art shortcomings and to reduce latency times of support cloud-based process control and/or analytics deterministically in real time in the industrial environment and systematically.